{"title":"Regulation-aware graph learning for drug repositioning over heterogeneous biological network","authors":"","doi":"10.1016/j.ins.2024.121360","DOIUrl":null,"url":null,"abstract":"<div><p>Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence techniques in discovering novel drug-disease associations (DDAs), many algorithms primarily focus on incorporating biological knowledge of drugs and diseases into DDA networks, often overlooking the rich connectivity patterns inherent in heterogeneous biological networks. In this study, we leveraged diverse connectivity patterns to gain new insights into the regulatory mechanisms of drugs acting on target proteins in diseases. We defined a set of meta-paths to reveal different regulatory mechanisms, each corresponding to distinct connectivity patterns. For each meta-path, we constructed a regulation graph through random-walk sampling of its instances in the network and obtained drug and disease embeddings through regulation-aware graph representation learning. Subsequently, we proposed a novel multi-view attention mechanism to enhance drug and disease representations. The task of predicting DDAs was accomplished using the XGBoost classifier based on the final representations of drugs and diseases. The experimental results demonstrated the superior performance of our method, RGLDR, on three benchmark datasets under ten-fold cross-validation, outperforming state-of-the-art DR algorithms across several evaluation metrics. Furthermore, case studies on two diseases indicated that RGLDR is a promising DR tool that leverages meaningful connectivity patterns for improved efficacy.</p></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S002002552401274X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"N/A","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Drug repositioning (DR) is crucial for identifying new disease indications for existing drugs and enhancing their clinical utility. Despite the effectiveness of various artificial intelligence techniques in discovering novel drug-disease associations (DDAs), many algorithms primarily focus on incorporating biological knowledge of drugs and diseases into DDA networks, often overlooking the rich connectivity patterns inherent in heterogeneous biological networks. In this study, we leveraged diverse connectivity patterns to gain new insights into the regulatory mechanisms of drugs acting on target proteins in diseases. We defined a set of meta-paths to reveal different regulatory mechanisms, each corresponding to distinct connectivity patterns. For each meta-path, we constructed a regulation graph through random-walk sampling of its instances in the network and obtained drug and disease embeddings through regulation-aware graph representation learning. Subsequently, we proposed a novel multi-view attention mechanism to enhance drug and disease representations. The task of predicting DDAs was accomplished using the XGBoost classifier based on the final representations of drugs and diseases. The experimental results demonstrated the superior performance of our method, RGLDR, on three benchmark datasets under ten-fold cross-validation, outperforming state-of-the-art DR algorithms across several evaluation metrics. Furthermore, case studies on two diseases indicated that RGLDR is a promising DR tool that leverages meaningful connectivity patterns for improved efficacy.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.